FACULTY OF ENGINEERING

This project focuses on the design and fabrication of a hybrid (solar–electric) dryer for agricultural materials. The aim is to develop a low-cost and efficient drying system that utilizes both solar and electrical energy to ensure continuous operation under varying weather conditions. The dryer was designed with major components, including a solar collector, drying chamber, heating element, and forced draft fan powered by both photovoltaic and electrical sources. Locally available materials such as sheet metal, glass, insulation, and mild steel were used in the fabrication process to promote affordability and sustainability. Performance tests were carried out using cassava chips as the sample material, and relevant parameters such as temperature variation, drying time, and moisture reduction were recorded. Results showed that the hybrid dryer achieved faster and more uniform drying compared to traditional open-sun drying. The system proved reliable, environmentally friendly, and capable of maintaining operation during periods of low sunlight. This innovation demonstrates a practical approach to reducing post-harvest losses and improving the preservation of agricultural produce in regions with inconsistent power supply.

Highway accidents are a significant concern worldwide, leading to substantial loss of life, property, and economic productivity. This study aims to conduct a comprehensive analysis of highway accidents by investigating their frequency, underlying causes, and exploring effective preventive measures. Through an extensive review of historical accident data, this research will assess key factors contributing to highway accidents, including human behavior, vehicle conditions, environmental influences, and road infrastructure. Special attention will be given to identifying accident-prone areas (blackspots) and determining the most common types of accidents, as well as the timeframes in which they frequently occur. To better understand the root causes of these accidents, the study will employ statistical analysis and machine learning techniques on data sourced from government databases and highway safety reports. The study will focus on critical factors such as driver error, vehicle malfunctions, poor road design, adverse weather conditions, and inadequate traffic management systems. It will also analyze the effectiveness of existing safety measures like traffic signals, road signage, and speed limits, while proposing new, data-driven interventions for improving highway safety. The expected results of this research include a clearer identification of high-risk areas and times for highway accidents, as well as the discovery of key accident causative factors. Based on these findings, the study will propose targeted solutions, such as enhancing road infrastructure, increasing public awareness campaigns on safe driving, implementing stricter vehicle inspection protocols, and adopting advanced traffic monitoring systems. Ultimately, the results are expected to provide actionable insights for policymakers and highway authorities to reduce accident rates and improve overall road safety.

This study investigates the use of guava leaf extract (Psidium guajava) as an organic corrosion inhibitor for low-carbon steel in a carbonic medium. Corrosion of steel remains a critical global issue, with its annual cost estimated to exceed $2.5 trillion, representing approximately 3.4% of the world’s GDP. This necessitates an environmentally friendly alternative to traditional toxic synthetic inhibitors. The research method involved preparing the extract and conducting corrosion tests on mild steel coupons (with a carbon content of less than 0.25%). Experiments were carried out in a 1M HCl acidic medium using techniques such as weight loss measurements, potential dynamic polarization, and EIS, across a temperature range of 25–60°C and a pH range of 2–6. The study focused on short-term exposure (up to 24 hours) to assess the initial inhibition efficiency. The guava leaf extract proved to be a highly effective inhibitor. Statistical validation via ANOVA established the model's significance with an extremely high Model F-value of 921.10 and a p-value of < 0.0001 for Inhibition Efficiency. The predictive power was robust, as indicated by a Predicted R² of 0.9945 being in reasonable agreement with the Adjusted R² of 0.9978 , and an Adequate Precision ratio of 79.032. Time and Temperature were confirmed as the only statistically significant model terms, suggesting the corrosion process is under kinetic control. The numerical optimization demonstrated that a near-maximum predicted efficiency could be achieved at the lowest inhibitor dosage, supporting the extract's high potency and cost-effectiveness. In conclusion, the guava leaf extract successfully performed as an environmentally sustainable corrosion inhibitor. The inhibition mechanism was determined to be chemisorption, driven by the active phenolic groups and aromatic rings in the extract, which form a compact protective barrier on the steel surface.

This study presents the design and implementation of a smart, lightweight, federated deep learning system that integrates solar-powered cameras for automated attendance, unauthorized entry prevention and real-time cyber threat detection in academic environments. Using TensorFlow Lite, Python, and a Flask-based web interface, the model achieved high accuracy in facial recognition while maintaining low computational and energy costs. A structured SQLite3 database supported efficient local data handling, while solar energy integration enabled autonomous and sustainable operation. This project validates the potential of combining renewable energy, artificial intelligence, and federated learning to enhance classroom management and IoT security in low resource settings.

Open drain channels play a vital role in stormwater management and flood prevention in urban and agricultural environments. However, factors such as complex channel geometry, surface roughness, sediment accumulation, and turbulence often reduce their efficiency, leading to flooding and waterlogging. This study aimed to analyze fluid flow in an open drainage channel using Computational Fluid Dynamics (CFD) to understand flow behavior, identify hydraulic inefficiencies, and recommend design improvements for enhanced drainage performance. The study focused on an open drainage channel within the University of Benin, Ugbowo Campus, Benin City, Nigeria. Data on channel dimensions, flow conditions, and physical characteristics were obtained through site investigations and measurements. A three-dimensional model of the channel was developed and simulated using CFD techniques in SolidWorks Flow Simulation. The governing equations of fluid flow, including the continuity and Navier-Stokes equations, were solved under appropriate boundary conditions to evaluate velocity distribution and free- surface flow characteristics. The simulation results showed that flow velocity increased gradually along the channel length, with the highest velocities occurring near the outlet region. The free surface remained stable throughout the simulation, indicating efficient water conveyance under the specified flow conditions. The study concluded that CFD is an effective tool for predicting flow behavior and evaluating the hydraulic performance of open drainage systems. The findings provide valuable insights for improving drainage design, reducing flood risks, and promoting sustainable stormwater management.

The production engineering department of the University of Benin has often relied on existing water supply from the faculty sunk borehole. This water has been investigated through physiochemical and biological testing to be below standard required quality for drinking. This has necessitated the development and installation of a water treatment for purification and dispensing facility in the departmental Annex of production engineering for safe sustainable water utilization. A conceptual design of the water treatment and dispensing facility was carried out and it comprises of cylindrical tanks stacked with one of the tank containing coarse aggregates of gravel, rock sand, fine aggregate of river sand, and activated carbon (charcoal) all separated with a filter mesh in the cylindrical tank. The other tank contains the clear water already treated by sedimentation tank containing the aggregated materials Tests and performance evaluation of the developed water treatment facility showed that that the volumes of samples of water taken from the water treatment and dispensing facility had minimal variation from one another. The samples had most volumes around the 50cl mark, while others were around 49cl sometimes successive difference of 0.1cl. The little variation in the volumes is due partly to excess drop in pressure of the reservoir water. The variance is 0.24 which is a mean of the respective deviations of the volumes. This value of the variance is very minimal, showing that the machine was able to discharge given volumes of water with considerable accuracy. The observed range of BODs for the treated water was (3.20mg/L to 3.88mg/L). Electrical conductivity values ranged from 18.00 to 65.00S/cm. It was inferred that there was no significant change in the pH value during the observation period; the observed values were in the range 6.9 to 7.5 for both samples of water before and after treatment. The physio-chemical characteristics of water samples in the study area suggested that there was no harmful
chemical contamination in both samples of water.

Cement manufacturing is one of the most energy-intensive industrial processes, requiring substantial thermal and electrical energy, particularly during clinkerization and cement grinding. In Nigeria, rising fuel costs, unstable power supply, and inefficient energy utilization have significantly increased production costs and environmental impacts, making effective energy management essential. This study presents a comprehensive energy audit of the BUA Cement Plant, Obu-Okpella, Edo State, with the aim of evaluating plant-wide energy performance, identifying inefficiencies and energy losses, and proposing strategies for improving efficiency and reducing production costs.
A detailed energy audit was conducted across major production stages, including raw material preparation, clinker production, cement grinding, and packaging of the finished product. Data were obtained through on-site equipment inspections, Central Control Room records, and production logs, supported by equipment inventories and design capacity data. Energy performance was evaluated using specific energy consumption (SEC) indicators, as well as mass and heat balance analyses. The results were further benchmarked against international best-practice standards to assess the plant’s relative performance.
The results reveal significant opportunities for energy optimization within the plant. The specific thermal energy consumption of the kiln was approximately 3.5 GJ/t of clinker, slightly above global best practice values of 2.8–3.0 GJ/t, indicating potential for improvement. Heat balance analysis showed total heat input and output of 3333 kJ/kg and 3342 kJ/kg clinker, respectively, with a minimal deviation of 0.27%, confirming data reliability. Kiln and cooler heat losses were estimated at 9 kJ/kg and 8 kJ/kg clinker, respectively, while total heat loss due to radiation and convection was 0.15 MJ/kg. The annual electrical energy intensity was 88.74 kWh/t of cement, within the global benchmark range of 80–120 kWh/t, with cement and raw mills identified as the largest electrical energy consumers. Furthermore, approximately 15.6% of total energy input in the 6000 t/day dry-process kiln system could be recovered through improved waste heat recovery and enhanced clinker cooler efficiency. The study concludes that while the plant operates within acceptable energy performance ranges, significant opportunities exist for further optimization. Implementation of improved kiln insulation, enhanced waste heat recovery systems, optimized process control, and structured energy management practices is recommended to reduce operational costs, improve overall efficiency, and support sustainable cement production.

This project focuses on the development of a 5kVA, 48V hybrid solar inverter system aimed at providing a reliable and sustainable energy solution for domestic and small-scale applications. The system integrates solar photovoltaic (PV) energy with utility grid supply to ensure an uninterrupted power source, with solar energy serving as the primary supply and the grid as backup. A hybrid charging approach was adopted to maintain adequate battery capacity during periods of low solar irradiation. The inverter was designed to deliver a stable output suitable for powering common household and office appliances, while incorporating essential protection features to enhance system safety, efficiency, and reliability. Performance evaluation showed that the inverter provided consistent output with smooth changeover between power sources and improved energy efficiency. The project demonstrates a cost-effective and environmentally friendly alternative to fuel-powered backup systems, reducing dependence on the national grid and promoting cleaner energy usage.

The growing concern over ineffective waste management in rapidly urbanizing areas has continued to threaten environmental sustainability, public health, and urban aesthetics— particularly in developing nations such as Nigeria. Conventional waste collection methods, which rely on manual inspections, static collection schedules, and minimal automation, are increasingly inadequate for modern cities. These traditional systems often result in overflowing waste bins, unhygienic surroundings, and increased exposure of sanitation workers to hazardous waste. To mitigate these challenges, the adoption of smart waste management systems has become a practical solution, harnessing Internet of Things (IoT) technologies to enhance operational efficiency, reduce health risks, and support global sustainability goals. This project presents the design and development of an improved smart dustbin system, which integrates sensors, automation, and wireless communication for efficient and hygienic waste collection. The system was engineered to address major limitations of existing smart waste bin models—such as lack of automation, low adaptability to environmental conditions, and poor sustainability—by introducing enhanced features that improve functionality, reliability, and user safety. The prototype incorporates ultrasonic sensors for detecting the fill level and load cell sensors for measuring the weight of accumulated waste. These sensors interface with an Arduino microcontroller, which interprets real-time data and initiates corresponding control actions. A key innovation in this design is the dual alert and communication mechanism, facilitated by a GSM module (SIM900D) that transmits SMS notifications and also initiates automated phone calls to designated waste management personnel once the bin reaches its full capacity. In addition, the system integrates a GPS module that tracks the exact location of the bin, simplifying collection logistics and enabling efficient route planning. To further enhance automation, the system features a linear actuator that performs self-compaction, reducing the waste volume and increasing the storage capacity before the next collection. Importantly, once the waste bin reaches its maximum threshold, the lid is automatically locked, preventing further deposit of waste and ensuring cleanliness until the bin is emptied and reset for operation. This mechanism helps prevent overflow and reduces contact with potentially contaminated waste. The system is powered by a 24W rechargeable lithium battery supported by a DC–DC converter, ensuring stable power supply and efficient energy usage. The software component was developed using Embedded C/C++ on the Arduino IDE, enabling real-time sensor monitoring, threshold detection, and GSM/GPS communication control. Comprehensive testing was carried out to evaluate sensor accuracy, power efficiency, communication reliability, and the responsiveness of the compaction and locking mechanisms. Results from both hardware and software testing confirmed that the system achieved reliable waste level detection, efficient data transmission, timely alert notifications, and effective compaction cycles. The automatic locking feature also performed accurately, preventing waste input once the bin reached capacity. By combining IoT technology, automation, and sustainable material selection, the improved smart dustbin system demonstrates a viable, scalable, and eco-friendly approach to modern waste management. Its ability to autonomously monitor fill levels, compress waste, lock when full, and communicate through both SMS and phone calls significantly enhances efficiency, hygiene, and sustainability. This prototype provides a foundation for large-scale implementation in residential, institutional, commercial, and municipal environments, contributing to cleaner cities and smarter waste management systems that align with sustainable development goals.